We extract machine specifications, pattern libraries, yarn configurations, and spare parts catalogues from Stoll. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Machine Specifications objects from stoll.com. All fields typed and schema-versioned.
"model_name": "ADF 830-24 ki W", "machine_class": "ADF", "gauge": "E 7.2", "working_width": "84 inches", "knitting_speed": "1.2 m/s", "needle_beds": 2, "carriage_type": "Multi-system", "weight": "1250 kg"
| # | model_name | machine_class | gauge | working_width | knitting_speed | needle_beds |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pattern Library objects from stoll.com. All fields typed and schema-versioned.
"pattern_id": "ST-2024-089", "collection_name": "TechTex Autumn", "stitch_type": "Jacquard", "gauge_compatibility": "E 14", "production_time": "42 minutes", "designer": "Stoll Fashion & Technology", "release_year": 2024
| # | pattern_id | collection_name | stitch_type | yarn_requirements | gauge_compatibility | production_time |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Spare Parts objects from stoll.com. All fields typed and schema-versioned.
"part_number": "024-991-002", "description": "Needle bed brush assembly", "category": "Maintenance", "availability_status": "In Stock", "machine_compatibility": "['CMS 530 ki', 'CMS 502 ki']", "replacement_interval": "2000 hours", "weight_grams": 450
| # | part_number | description | machine_compatibility | category | availability_status | list_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Yarn Configurations objects from stoll.com. All fields typed and schema-versioned.
"yarn_id": "YRN-774", "material_composition": "80% Merino, 20% Polyamide", "yarn_count": "Nm 28/2", "tension_settings": "Medium-High", "feeder_type": "Plating", "recommended_gauge": "E 12", "elasticity": "Low"
| # | yarn_id | material_composition | yarn_count | colour_code | tension_settings | feeder_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Software Features objects from stoll.com. All fields typed and schema-versioned.
"module_name": "knitelligence M1 Plus", "version_number": "v7.4.2", "license_type": "Enterprise Subscription", "release_date": "2025-11-14", "supported_machines": "['ADF', 'CMS']", "system_requirements": "Windows 11, 16GB RAM", "compatibility": "Backward compatible to v6.x"
| # | module_name | version_number | compatibility | license_type | description | release_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Our Stoll scraper parses highly technical product specifications, nested software compatibility matrices, and pattern metadata, turning complex industrial catalogues into queryable datasets.
Extract exact technical parameters including gauge configurations, working widths, knitting speeds, and carriage types across all ADF and CMS lines.
Capture pattern metadata, stitch types, yarn requirements, and production times from Stoll's digital fashion and technical textile collections.
Structure part numbers, compatibility lists, and maintenance intervals from dynamic tables and technical documentation.
Track knitelligence modules, version histories, machine compatibility matrices, and system requirements.
Extract highly specific needle bed configurations and gauge conversion charts for every machine class.
Capture material compositions, yarn counts, and recommended tension settings linked to specific knitting patterns.
Extract text and metadata from publicly available manuals, maintenance guides, and technical bulletins.
Track machine and part availability across different global regions and distributor networks.
Run pipelines on a scheduled cadence to detect new machine releases, software updates, and pattern additions.
Brief in. Clean data out.
Specify required machine classes, pattern categories, or software modules. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, handling dynamic content loading and complex table structures on stoll.com.
Schema validation, null-rate checks, and unit standardisation before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Industrial manufacturing sites present unique scraping challenges. Here is how we extract clean data from Stoll's complex architecture.
Stoll displays machine compatibility for spare parts and software using complex, dynamically loaded tables. We execute full browser sessions to render these matrices and normalise the relationships into flat, queryable arrays.
Much of Stoll's technical data is locked in PDF brochures. Our pipeline incorporates automated PDF parsing to extract tabular data, machine dimensions, and power consumption metrics directly from these documents.
Stoll publishes data in multiple languages. We target the primary English and German endpoints, applying consistent schema mapping so technical terms like 'Nadelbett' and 'Needle bed' align to the same database column.
Pattern libraries contain high-resolution images and technical drawings. We extract the source URLs and associate them with the structured metadata, ensuring your dataset includes both the visual and technical parameters.
We maintain a hash index of last-seen values for machine specs and software versions. Subsequent runs only push diffs, providing a clean changelog of Stoll's product updates.
Rival textile machinery manufacturers monitor Stoll's product releases, gauge configurations, and software capabilities to benchmark their own development.
Large scale knitting facilities aggregate machine specifications to plan factory floor layouts, power consumption, and production capacities.
Maintenance teams and third-party suppliers track part numbers and compatibility matrices to optimise inventory and procurement workflows.
Machine learning teams use structured pattern metadata, stitch types, and yarn requirements to train generative models for textile design.
Industry analysts track the release velocity of new technical textile patterns and software modules to forecast trends in flat knitting technology.
Used machinery dealers extract original specifications to accurately list and price refurbished Stoll CMS and ADF machines.
"Stoll defines the standard for flat knitting technology, but extracting their machine specifications and pattern data requires custom engineering."
Most teams underestimate the complexity of extracting technical textile data. We parse nested machine configurations, extract pattern metadata from dynamic catalogues, and structure yarn requirements so your engineers can focus on production analysis, not web scraping infrastructure.
Everything supported by our stoll.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration and deduplication. Playwright handles JavaScript rendering for Stoll's dynamic pattern libraries and compatibility matrices.
Custom parsers extract tabular data and technical specifications directly from Stoll's public PDF brochures and manuals, merging it with web data.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About stoll.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from stoll.com is generally permissible under applicable law. DataFlirt targets only public, non-authenticated machine specifications, pattern metadata, and spare parts catalogues. We do not extract proprietary software source code or circumvent authentication walls. Clients should review Stoll's ToS and consult legal counsel for specific use cases.
Yes. Our pipeline includes automated PDF parsing that extracts tabular data, dimensions, and technical specifications from publicly linked brochures, merging this data with the web-scraped records.
We extract the public metadata, technical parameters, and image URLs associated with the patterns. We do not extract the raw proprietary pattern files (.sint, .mdr) that require authenticated customer access.
Our schema is designed to normalise fields across different machine classes while retaining class-specific arrays for unique features like multi-system carriages or specific gauge conversions.
For industrial catalogues like Stoll, we typically configure weekly or monthly pipeline runs, as machine specifications and pattern libraries update less frequently than consumer retail sites. Custom cadences are available.
Absolutely. We provide a sample run of up to 50 machine configurations or 100 pattern records as part of the pre-engagement scoping process so you can validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off machine specification dump or continuous monitoring of pattern releases, we scope, build, and operate the pipeline. Tell us what you need.
